They are created by using the yield keyword
next() can be called explicitly or implicitly inside for loop
They can be finite or infinite
🤔 Python generators
What are they? How to use them?
#Python
🧵Let's find out 👇
They are created by using the yield keyword
next() can be called explicitly or implicitly inside for loop
They can be finite or infinite
The state of function is remembered.
For example, the number is incremented and sent back from yield at the consecutive call next()
For example, we don't need to store 1mio elements in memory to do something with each element
It executes only when next() is called
For example, that's why Exception is raised only on the next() call
- __iter__ -> to enable iteration
- __next__ -> to enable next element access
More from Machine learning
This is a Twitter series on #FoundationsOfML.
❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
1️⃣ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
❓ Today, I want to start discussing the different types of Machine Learning flavors we can find.
This is a very high-level overview. In later threads, we'll dive deeper into each paradigm... 👇🧵
Last time we talked about how Machine Learning works.
Basically, it's about having some source of experience E for solving a given task T, that allows us to find a program P which is (hopefully) optimal w.r.t. some metric
I'm starting a Twitter series on #FoundationsOfML. Today, I want to answer this simple question.
— Alejandro Piad Morffis (@AlejandroPiad) January 12, 2021
\u2753 What is Machine Learning?
This is my preferred way of explaining it... \U0001f447\U0001f9f5
According to the nature of that experience, we can define different formulations, or flavors, of the learning process.
A useful distinction is whether we have an explicit goal or desired output, which gives rise to the definitions of 1️⃣ Supervised and 2️⃣ Unsupervised Learning 👇
1️⃣ Supervised Learning
In this formulation, the experience E is a collection of input/output pairs, and the task T is defined as a function that produces the right output for any given input.
👉 The underlying assumption is that there is some correlation (or, in general, a computable relation) between the structure of an input and its corresponding output and that it is possible to infer that function or mapping from a sufficiently large number of examples.
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1/ Some initial thoughts on personal moats:
Like company moats, your personal moat should be a competitive advantage that is not only durable—it should also compound over time.
Characteristics of a personal moat below:
2/ Like a company moat, you want to build career capital while you sleep.
As Andrew Chen noted:
3/ You don’t want to build a competitive advantage that is fleeting or that will get commoditized
Things that might get commoditized over time (some longer than
4/ Before the arrival of recorded music, what used to be scarce was the actual music itself — required an in-person artist.
After recorded music, the music itself became abundant and what became scarce was curation, distribution, and self space.
5/ Similarly, in careers, what used to be (more) scarce were things like ideas, money, and exclusive relationships.
In the internet economy, what has become scarce are things like specific knowledge, rare & valuable skills, and great reputations.
Like company moats, your personal moat should be a competitive advantage that is not only durable—it should also compound over time.
Characteristics of a personal moat below:
I'm increasingly interested in the idea of "personal moats" in the context of careers.
— Erik Torenberg (@eriktorenberg) November 22, 2018
Moats should be:
- Hard to learn and hard to do (but perhaps easier for you)
- Skills that are rare and valuable
- Legible
- Compounding over time
- Unique to your own talents & interests https://t.co/bB3k1YcH5b
2/ Like a company moat, you want to build career capital while you sleep.
As Andrew Chen noted:
People talk about \u201cpassive income\u201d a lot but not about \u201cpassive social capital\u201d or \u201cpassive networking\u201d or \u201cpassive knowledge gaining\u201d but that\u2019s what you can architect if you have a thing and it grows over time without intensive constant effort to sustain it
— Andrew Chen (@andrewchen) November 22, 2018
3/ You don’t want to build a competitive advantage that is fleeting or that will get commoditized
Things that might get commoditized over time (some longer than
Things that look like moats but likely aren\u2019t or may fade:
— Erik Torenberg (@eriktorenberg) November 22, 2018
- Proprietary networks
- Being something other than one of the best at any tournament style-game
- Many "awards"
- Twitter followers or general reach without "respect"
- Anything that depends on information asymmetry https://t.co/abjxesVIh9
4/ Before the arrival of recorded music, what used to be scarce was the actual music itself — required an in-person artist.
After recorded music, the music itself became abundant and what became scarce was curation, distribution, and self space.
5/ Similarly, in careers, what used to be (more) scarce were things like ideas, money, and exclusive relationships.
In the internet economy, what has become scarce are things like specific knowledge, rare & valuable skills, and great reputations.
